Predicting the diagnosis of autism spectrum disorder using gene pathway analysis

Abstract

Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD.

Figure 1

(a and b) Flow charts show the subjects used in the analyses. Key: AGRE, Autism Genetic Research Exchange; SFARI, Simons Foundation Autism Research Initiative; WTBC, Wellcome Trust 1958 normal birth cohort; CEU, of Central (Western and Northern) European origin; HAN, of Han Chinese origin; TSI, of Tuscan Italian origin; For panels 1a and b: ‘red boxes'—samples used in developing the predictive algorithm; ‘blue boxes'—samples used to investigate different ethnic groups; ‘green boxes'—validation sets; ‘light green boxes'—relatives assessed, including parents and unaffected siblings. Numbers in brackets represent numbers of males/females.

Figure 2

Cumulative coefficient estimation error and percentage classification error as a function of P-value; P=0.005 provides good trade-off between classification performance and cumulative regression coefficient error.

Figure 3

(a) Genetic-based classification of CEU population (AGRE and Controls) for ASD and non-ASD individuals, showing Gaussian approximation of distribution of individuals. As both the mapped ASD and control populations were well approximated by normal distributions, the asymptotic Test Positive Predictive Value (PPV) and Negative Predictive Value (NPV) was determined. For individuals with CEU ancestry, the PPV and NPV were 96.72% and 94.74%, respectively. (Note the test was substantially less predictive on individuals with different ancestry, that is, Han Chinese). (b) Genetic-based classification of CEU population, including first-degree relatives (parents and siblings of ASD children). Note that the distribution of relatives of ASD children maps between the ASD and the control groups, with no difference found between mothers and fathers (see Supplementary material S5). Key: ASD, autism spectrum disorder; relatives, first-degree relatives (parents and siblings); Siblings, siblings of ASD cases not meeting criteria for ASD; Autism Classifier Score, scores for each individual derived from the predictive algorithm, with greater values representing greater risk for autism.